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About healthiar

About healthiar 1/5

healthiar is an R package (= collection of R functions)

The healthiar functions allow you to quantify and monetize the health impacts of environmental stressors (air pollution & noise)

About healthiar 2/5

Figure: healthiar overview. CBA = Cost-benefit analysis; DALY = disability-adjusted life years; GBD = global burden of disease; MDI = multidimensional deprivation index; PAF = population attributable fraction; PIF = population impact fraction; YLD = years lived with disability; YLL = years of life lost

About healthiar 3/5

Let’s jump right in, with an example of a healthiar R function call

About healthiar 4/5

Selection of healthiar core family members (functions)

  • attribute_health() to env. exposure with either relative or absolute risk
  • compare() two scenarios
  • summarize_uncertainty() Monte Carlo simulation
  • monetize() health impacts

About healthiar 5/5

Installation & getting started with healthiar: see the README file

README file of the healthiar R package on GitHub

healthiar in RStudio 1/2

Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

healthiar in RStudio 2/2

Any arguments without a = symbol after the name have no default and must be user-specified

Example: attribute_health() with RR

Refresher - Burden of disease with relative risk

attribute_health() with RR

Goal: attribute COPD cases to air pollution

results_pm_copd <-
  attribute_health(
    erf_shape = "log_linear", # Alternatives: "linear", ...
    rr_central = 1.369, 
    rr_increment = 10,  # μg / m^3
    exp_central = 8.85, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 30747 # baseline health data: COPD incidence
  ) 

Tip

healthiar comes with some example data that start with exdat_ that allow you to test functions.

results_pm_copd <- attribute_health(
    erf_shape = "log_linear",
    rr_central = exdat_pm_copd$relative_risk, 
    rr_increment = 10, 
    exp_central = exdat_pm_copd$mean_concentration,
    cutoff_central = exdat_pm_copd$cut_off_value,
    bhd_central = exdat_pm_copd$incidence
  ) 

Output structure

Every attribute output initially consists of two main lists (“folders”), and additional sub-lists (“sub-folders”)

  • health_main contains the main results

  • health_detailed contains more detailed results and additional information

    • impact_raw contains detailed results

    • input_table contains the input data as entered in the function call

    • input_args = function arguments (list) as used by R in the background

The output tables are in the tibble format, which is a modern version of the original data frame, and can be used like a data frame

How to access the results

Tip

Different ways exist and you might have a personal preference!

Go to the Environment tab in RStudio …

… and click on a variable to “open” it.

Alternatively, you can use View(results_noise_ha), which has the same effect.

results_pm_copd$health_main$impact_rounded

Note: after typing the $ sign you can see all available options by pressing the tab key and use the arrows & tab keys to select an option (or alternatively use the mouse)

Let’s inspect the main results

results_pm_copd$health_main
impact_rounded impact pop_fraction erf_ci rr exp bhd
3502 3501.962 0.1138961 central 1.369 8.85 30747

Some relevant columns include:

  • impact_rounded Rounded attributable health impact/burden
  • impact Raw impact/burden
  • pop_fraction Population attributable fraction (PAF)
  • erf_ci Specifies whether rr_central, ..._lower or ..._upper was used to obtain impact
  • rr Raw RR used in calculation
  • exp Exposure
  • bhd Baseline health data

Example: attribute_health() with RR & uncertainty

attribute_health() with RR & uncertainty

Goal: attribute COPD cases to PM2.5 exposure

results_pm_copd <- attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, 
    rr_upper = 1.664,
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000
) 

Let’s inspect the detailed results

In health_detailed each row contains results for a different/unique input argument uncertainty combination:

  • rr_central with exp_central and bhd_central

  • rr_lower with exp_central and bhd_central

results_pm_copd$health_detailed$impact_raw
exp_ci bhd_ci erf_ci pop_fraction impact geo_id_disaggregated is_lifetable prop_pop_exp rr_increment erf_shape approach_risk exposure_dimension exposure_type exp rr bhd cutoff_ci cutoff duration_ci duration pop_fraction_type rr_conc impact_rounded
central central central 0.1138961 3501.9619 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.369 30747 central 5 central 1 paf 1.128536 3502
central lower central 0.1138961 3189.0894 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.369 28000 central 5 central 1 paf 1.128536 3189
central upper central 0.1138961 3644.6736 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.369 32000 central 5 central 1 paf 1.128536 3645
central central lower 0.0440064 1353.0658 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.124 30747 central 5 central 1 paf 1.046032 1353
central lower lower 0.0440064 1232.1801 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.124 28000 central 5 central 1 paf 1.046032 1232
central upper lower 0.0440064 1408.2058 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.124 32000 central 5 central 1 paf 1.046032 1408
central central upper 0.1780300 5473.8882 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.664 30747 central 5 central 1 paf 1.216589 5474
central lower upper 0.1780300 4984.8398 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.664 28000 central 5 central 1 paf 1.216589 4985
central upper upper 0.1780300 5696.9598 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.85 1.664 32000 central 5 central 1 paf 1.216589 5697
lower central central 0.0899213 2764.8092 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.369 30747 central 5 central 1 paf 1.098806 2765
lower lower central 0.0899213 2517.7955 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.369 28000 central 5 central 1 paf 1.098806 2518
lower upper central 0.0899213 2877.4806 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.369 32000 central 5 central 1 paf 1.098806 2877
lower central lower 0.0344604 1059.5528 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.124 30747 central 5 central 1 paf 1.035690 1060
lower lower lower 0.0344604 964.8902 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.124 28000 central 5 central 1 paf 1.035690 965
lower upper lower 0.0344604 1102.7316 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.124 32000 central 5 central 1 paf 1.035690 1103
lower central upper 0.1416706 4355.9450 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.664 30747 central 5 central 1 paf 1.165054 4356
lower lower upper 0.1416706 3966.7760 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.664 28000 central 5 central 1 paf 1.165054 3967
lower upper upper 0.1416706 4533.4583 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 8.00 1.664 32000 central 5 central 1 paf 1.165054 4533
upper central central 0.1453304 4468.4726 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.369 30747 central 5 central 1 paf 1.170043 4468
upper lower central 0.1453304 4069.2501 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.369 28000 central 5 central 1 paf 1.170043 4069
upper upper central 0.1453304 4650.5716 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.369 32000 central 5 central 1 paf 1.170043 4651
upper central lower 0.0567717 1745.5580 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.124 30747 central 5 central 1 paf 1.060189 1746
upper lower lower 0.0567717 1589.6063 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.124 28000 central 5 central 1 paf 1.060189 1590
upper upper lower 0.0567717 1816.6929 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.124 32000 central 5 central 1 paf 1.060189 1817
upper central upper 0.2247829 6911.4001 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.664 30747 central 5 central 1 paf 1.289961 6911
upper lower upper 0.2247829 6293.9214 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.664 28000 central 5 central 1 paf 1.289961 6294
upper upper upper 0.2247829 7193.0531 1 FALSE 1 10 log_linear relative_risk 1 population_weighted_mean 10.00 1.664 32000 central 5 central 1 paf 1.289961 7193

Example: summarize_uncertainty() for overall confidence intervals

summarize_uncertainty() for overall confidence intervals

You can do a Monte Carlo simulation via the summarize_uncertainty function.

The outcome added to inputted results variable (results_pm_copd in this case)

Two folders are added:

  • uncertainty_main contains the central estimate (median of simulated estimates) and the corresponding 95% confidence intervals obtained through the Monte Carlo assessment

  • uncertainty_detailed contains all n_sim simulations

results_pm_copd <- 
  summarize_uncertainty(
    results = results_pm_copd,
    n_sim = 1000
)

Tip

This is work in progress!

Distributions used in the simulations (to be added to function documentation):

  • Relative risk (per increment) gamma distribution

  • Exposure, baseline health data & cutoff normal distribution

  • Disability weight beta distribution

  • Relative risk user-defined ERF to be added

  • Absolute risk user-defined ERF normal distribution

Let’s inspect the main results

results_pm_copd$uncertainty_main
central_estimate lower_estimate upper_estimate
3361.85 1179.711 5689.718

Let’s inspect the detailed results

results_pm_copd$uncertainty_detailed
rr exp bhd rr_conc paf impact_total
1.285688 9.508392 31403.72 1.11996 0.1071111 3363.688
1.531341 8.329302 30356.39 1.152434 0.1322713 4015.278
1.138642 9.209543 28722.15 1.056176 0.0531883 1527.683
1.379912 9.527935 31147.49 1.156975 0.1356768 4225.991
1.609170 9.725841 30868.47 1.252094 0.2013377 6214.985
1.426400 8.411436 29991.41 1.128804 0.1141064 3422.211
1.192879 8.332883 31521.85 1.060544 0.0570876 1799.506
1.133884 8.737630 29288.22 1.048083 0.0458772 1343.662
1.536306 8.931032 29886.18 1.183873 0.1553145 4641.758
1.412146 8.406493 31063.27 1.124751 0.1109143 3445.360
1.417912 8.477226 29683.65 1.129099 0.1143377 3393.960
1.377327 9.417160 30554.96 1.1519 0.1318693 4029.261
1.286310 8.765111 29136.53 1.099436 0.0904425 2635.181
1.546691 9.343722 31961.40 1.20857 0.1725756 5515.758
1.478402 8.595755 32563.51 1.150941 0.1311460 4270.576
1.354317 9.322901 31823.58 1.140096 0.1228809 3910.509
1.297481 8.891400 32220.22 1.106655 0.0963758 3105.249
1.332347 8.791603 30143.28 1.114936 0.1030874 3107.393
1.223978 7.898470 32190.05 1.06033 0.0568971 1831.521
1.350225 8.124512 31202.96 1.098362 0.0895534 2794.332
1.139003 9.212674 29513.46 1.05636 0.0533534 1574.643
1.205776 8.189381 30416.80 1.061498 0.0579349 1762.193
1.209341 8.306106 32429.23 1.064857 0.0609071 1975.172
1.318152 8.933384 31125.40 1.114774 0.1029576 3204.595
1.321549 9.317071 30829.88 1.127905 0.1134004 3496.121
1.344183 8.700120 31836.60 1.115658 0.1036680 3300.437
1.477832 9.273465 29406.00 1.18165 0.1537254 4520.449
1.458768 8.207489 31741.91 1.128752 0.1140656 3620.659
1.439575 9.361821 29868.83 1.172247 0.1469372 4388.841
1.353460 9.239273 28925.77 1.136903 0.1204172 3483.161
1.401861 8.577939 30981.19 1.12847 0.1138447 3527.043
1.198644 9.006337 32037.70 1.075291 0.0700190 2243.248
1.333433 9.464992 30018.33 1.137102 0.1205715 3619.354
1.339450 8.338405 29214.03 1.102486 0.0929593 2715.717
1.533942 8.394773 31386.63 1.15632 0.1351873 4243.074
1.211297 8.865382 29850.50 1.07691 0.0714177 2131.854
1.418315 8.634695 30187.88 1.135441 0.1192852 3600.967
1.242320 8.723763 31369.69 1.084152 0.0776203 2434.924
1.350507 9.178648 31440.92 1.133783 0.1179973 3709.943
1.302968 8.961486 30422.95 1.110531 0.0995302 3028.004
1.356083 9.628013 32025.18 1.151389 0.1314841 4210.801
1.400387 8.327527 30339.46 1.118573 0.1060040 3216.103
1.220972 9.115051 31576.35 1.085625 0.0788717 2490.481
1.156492 8.862623 30829.04 1.057766 0.0546114 1683.616
1.218216 8.354771 31989.60 1.068461 0.0640740 2049.701
1.391985 7.984684 31719.73 1.103749 0.0939970 2981.560
1.414935 9.188174 30893.99 1.156461 0.1352931 4179.745
1.293503 8.807688 30457.87 1.102954 0.0933442 2843.067
1.675114 8.191074 31813.84 1.178947 0.1517853 4828.874
1.183939 8.999369 30533.11 1.06986 0.0652986 1993.770
1.404241 8.800046 31356.47 1.137702 0.1210352 3795.238
1.424605 9.665176 29133.15 1.179509 0.1521893 4433.755
1.369328 8.377727 31146.49 1.112009 0.1007271 3137.295
1.492353 9.278921 29832.61 1.186857 0.1574383 4696.795
1.659857 8.522931 31572.71 1.195445 0.1634911 5161.856
1.295024 8.105655 32480.59 1.083601 0.0771514 2505.924
1.240427 9.450927 29169.46 1.100646 0.0914430 2667.344
1.265808 7.687133 31790.43 1.065388 0.0613744 1951.119
1.448872 8.988882 30491.86 1.159399 0.1374841 4192.147
1.361141 9.131160 29423.94 1.135841 0.1195951 3518.960
1.198617 9.056507 30884.86 1.076259 0.0708556 2188.366
1.256037 8.541922 31381.55 1.084091 0.0775685 2434.219
1.362793 8.379576 29713.20 1.110278 0.0993245 2951.249
1.415730 9.094852 30580.49 1.152986 0.1326871 4057.636
1.311285 8.904021 31733.36 1.111602 0.1003972 3185.942
1.257928 8.268175 31101.58 1.077877 0.0722506 2247.106
1.408206 8.354344 30916.77 1.121677 0.1084777 3353.780
1.517698 8.304259 29473.72 1.147806 0.1287723 3795.400
1.422765 7.879501 29742.09 1.106865 0.0965476 2871.526
1.317354 9.387687 31197.23 1.128552 0.1139090 3553.645
1.390649 8.778709 30112.01 1.132707 0.1171595 3527.909
1.295307 8.655817 28398.93 1.099212 0.0902574 2563.213
1.395169 9.603162 31293.30 1.165666 0.1421212 4447.441
1.276655 9.161596 32740.78 1.10699 0.0966492 3164.371
1.546330 9.644315 32004.02 1.224385 0.1832632 5865.157
1.298726 10.012797 30335.59 1.139998 0.1228055 3725.376
1.581896 8.609282 29488.03 1.180019 0.1525559 4498.573
1.329638 8.958514 30421.52 1.119387 0.1066536 3244.564
1.387484 10.210339 31184.47 1.186057 0.1568702 4891.915
1.618744 9.310252 30655.81 1.230725 0.1874708 5747.070
1.367207 9.965514 30882.52 1.168016 0.1438471 4442.361
1.529293 9.153735 31624.09 1.192978 0.1617619 5115.572
1.285948 9.019776 29893.38 1.106383 0.0961537 2874.358
1.355785 9.563891 31039.88 1.149028 0.1296990 4025.842
1.425144 9.392363 31274.19 1.16837 0.1441067 4506.820
1.435735 8.743207 32429.35 1.144975 0.1266188 4106.166
1.330381 8.849186 27760.64 1.116145 0.1040591 2888.747
1.283693 8.644385 30337.79 1.095286 0.0869963 2639.274
1.429891 9.525869 27957.70 1.175677 0.1494264 4177.618
1.325542 8.579015 30282.23 1.106127 0.0959444 2905.411
1.434679 8.935600 31701.95 1.152637 0.1324239 4198.094
1.403908 8.210236 30399.22 1.115062 0.1031893 3136.873
1.376632 9.551326 30439.68 1.156593 0.1353915 4121.274
1.274944 9.517273 31507.04 1.115972 0.1039201 3274.216
1.498075 8.357447 30029.44 1.14534 0.1268969 3810.643
1.482485 8.246144 31988.57 1.136334 0.1199768 3837.888
1.420453 9.239489 32093.30 1.160436 0.1382549 4437.056
1.409355 8.755419 30888.66 1.137531 0.1209033 3734.542
1.445617 8.809513 31192.22 1.150728 0.1309846 4085.700
1.632728 9.208270 32800.93 1.229136 0.1864203 6114.760

Example: attribute_health() with RR & user-defined ERF

attribute_health() with RR & user-defined ERF

A user-defined exposure-response function can be fed to the erf_eq_... arguments

  • Any function of the form intercept + a x c^1 + b x c^2 + …

  • Any other (non-linear) function of the function type, as obtained from e.g. splinefun() or approxfun()

    • E.g. MR-BRT curves from Global Burden of Disease study
results_pm_copd_mr_brt <- attribute_health(
    exp_central = 8.85,
    bhd_central = 30747,
    erf_eq_central = 
      stats::splinefun(
        x = c(0, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110),
        y = c(1.00, 1.04, 1.08, 1.12, 1.16, 1.20, 1.23, 1.35, 1.45, 1.53, 1.60),
        method = "natural"),
    cutoff_central = 0
)

Let’s inspect the main results

results_pm_copd_mr_brt$health_main
impact_rounded pop_fraction erf_ci exp bhd
2033 0.0661067 central 8.85 30747

Example: attribute_health() with AR

Refresher - Burden of disease with absolute risk

attribute_health() with AR

Goal: attribute cases of high annoyance (HA) to noise exposure

Source input data: NIPH
results_noise_ha <- attribute_health(
    approach_risk = "absolute_risk",
    exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
    pop_exp = c(387500, 286000, 191800, 72200, 7700),
    erf_eq_central = "78.9270-3.1162*c+0.0342*c^2")

Results per noise exposure band

results_noise_ha$health_detailed$impact_raw
exposure_dimension exp pop_exp impact
1 57.5 387500 49674.594
2 62.5 286000 50788.595
3 67.5 191800 46813.105
4 72.5 72200 23657.232
5 77.5 7700 3298.314

Example: Iteration with attribute_health()

Iteration with attribute_health()

Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …

results_iteration <- attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = list(11, 11, 10, 8, 7),
    bhd_central = list(4000, 2500, 3000, 1500, 500)
)

Here the we want to aggregate results by language region ("Ger", "Fra", "Ita")

results_iteration <- attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500))
)

Use as.list() if you input vectors.

Tip

  • For iterations, enter geo unit-specific inputs as a list

  • Feed unique geo ID’s as a vector to the geo_id_disaggregated argument (e.g. municipality names)

  • Optional: aggregate geo unit-specific results by providing higher-level ID’s (e.g. region names) to the geo_id_aggregated argument (as a vector)

Let’s check the main iteration results!

Tip

The main output contains aggregated results if available, or disaggregated results if no aggregation ID was provided

results_iteration$health_main
geo_id_aggregated impact_rounded erf_ci exp_ci bhd_ci
Fra 466 central central central
Ger 1116 central central central
Ita 135 central central central

Let’s check the detailed iteration results!

results_iteration$health_detailed$impact_raw
geo_id_disaggregated geo_id_aggregated impact_rounded
Zurich Ger 687
Basel Ger 429
Geneva Fra 436
Ticino Ita 135
Valais Fra 30

Example: compare() two scenarios

compare() two scenarios

  1. Use attribute_health() to calculate burden of scenarios A & B
scenario_A <- attribute_health(
    exp_central = 8.85,   # EXPOSURE 1
    cutoff_central = 5, 
    bhd_central = 25000,
    approach_risk = "relative_risk",
    erf_shape = "log_linear",
    rr_central = 1.118,
    rr_increment = 10)
scenario_B <- attribute_health(
    exp_central = 6,     # EXPOSURE 2
    cutoff_central = 5, 
    bhd_central = 25000,
    approach_risk = "relative_risk",
    erf_shape = "log_linear",
    rr_central = 1.118,
    rr_increment = 10)
  1. Use compare() to compare scenarios A & B
results_comparison <- compare(
  
  approach_comparison = "delta", # or "pif" (population impact fraction)
  
  output_attribute_1 = scenario_A,
  
  output_attribute_2 = scenario_B
)

Let’s check the comparison results!

The compare() results are very similar to attribute_health() results:

  • health_main contains main comparison results

  • health_detailed

    • impact_raw raw comparison results

    • scenario_1 contains results of scenario 1 (scenario A in our case)

    • scenario_2 contains results of scenario 2 (scenario B in our case)

results_comparison$health_main
impact impact_rounded impact_1 impact_2 bhd exp_1 exp_2 rr_conc_1 rr_conc_2
773.5564 774 1050.86 277.304 25000 8.85 6 1.043879 1.011217

Example: monetize() health impacts

monetize() health impacts

Different monetization pathways / options are available

  • Discounting yes/no
  • Inflation yes/no
  • Stand-alone use the function can either monetize a healthiar output or an “external” health impact
results_monetization <- 
  monetize(
    output_healthiar = results_pm_copd,
    discount_shape = "exponential",
    discount_rate = 0.03,
    discount_years = 5,
    valuation = 20)

Let’s check the monetize() output

monetize() adds two main lists (“folders”) to the inputted health impacts

  • monetization_main contains total results

  • monetization_detailed contains detailed results

    • by_year yearly results
results_monetization$monetization_main
impact monetized_impact discount_rate valuation monetized_impact_before_inflation_and_discount monetized_impact_after_inflation_and_discount
3501.962 60416.46 0.03 20 70039.24 60416.46

Additional features

Additional existing healthiar functions

  • attribute_lifetable() for YLL
  • attribute_health with the arguments dw & duration for YLD
  • get_daly as the sum of YLL and YLD
  • prepare_exposure() combines spatial exposure data and spatial geographic units data to obtain (pop-weighted) mean exposure
  • attribute_health() with exposure categories and RR
  • socialize determine burden attributable to differences in exposure that are caused by differences in a social indicator (e.g. share of health impacts attributable to the fact that a person lives in a poor neighborhood with high exposure instead of a rich neighborhood with low exposure)
  • attribute_mod() modify an existing healthiar assessment
  • cba() cost-benefit analysis

Post-healthiar workflow

Export results

write.csv(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.csv")
save(results_pm_copd, file = "exported_results/results_pm_copd.Rdata")
openxlsx::write.xlsx(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.xlsx")

Exported to .xlsx format

Visualize results

Visualization is out of scope of healthiar. You can visualize in

Q & A

Happy coding! : )